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基于蛋白质相互作用的冠心病发病全基因组分析。

Protein interaction-based genome-wide analysis of incident coronary heart disease.

作者信息

Jensen Majken K, Pers Tune H, Dworzynski Piotr, Girman Cynthia J, Brunak Søren, Rimm Eric B

机构信息

Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA.

出版信息

Circ Cardiovasc Genet. 2011 Oct;4(5):549-56. doi: 10.1161/CIRCGENETICS.111.960393. Epub 2011 Aug 31.

Abstract

BACKGROUND

Network-based approaches may leverage genome-wide association (GWA) analysis by testing for the aggregate association across several pathway members. We aimed to examine if networks of genes that represent experimentally determined protein-protein interactions (PPIs) are enriched in genes associated with risk of coronary heart disease (CHD).

METHODS AND RESULTS

Genome-wide association analyses of approximately ≈700,000 single-nucleotide polymorphisms in 899 incident CHD cases and 1823 age- and sex-matched controls within the Nurses' Health and the Health Professionals Follow-up Studies were used to assign genewise P values. A large database of PPIs was used to assemble 8351 unbiased protein complexes and corresponding gene sets. Superimposed genewise P values were used to rank gene sets based on their enrichment in genes associated with CHD. After correcting for the number of complexes tested, 1 gene set was overrepresented in CHD-associated genes (P=0.002). Centered on the β1-adrenergic receptor gene (ADRB1), this complex included 18 protein interaction partners that have not been identified as candidate loci for CHD. Of the 19 genes in the top complex, 5 are involved in abnormal cardiovascular system physiological features based on knockout mice (4-fold enrichment; Fisher exact test, P=0.006). Ingenuity pathway analysis revealed that canonical pathways, especially related to blood pressure regulation, were significantly enriched in the genes from the top complex.

CONCLUSIONS

The integration of a GWA study with PPI data successfully identifies a set of candidate susceptibility genes for incident CHD that would have been missed in single-marker GWA analysis.

摘要

背景

基于网络的方法可通过测试多个通路成员间的总体关联来利用全基因组关联(GWA)分析。我们旨在研究代表实验确定的蛋白质-蛋白质相互作用(PPI)的基因网络是否在与冠心病(CHD)风险相关的基因中富集。

方法与结果

在护士健康研究和卫生专业人员随访研究中,对899例冠心病发病病例和1823例年龄及性别匹配的对照进行了约70万个单核苷酸多态性的全基因组关联分析,以确定基因层面的P值。使用一个大型PPI数据库组装了8351个无偏倚的蛋白质复合物及相应的基因集。利用叠加的基因层面P值,根据基因集在与冠心病相关基因中的富集程度对其进行排序。在对测试的复合物数量进行校正后,1个基因集在与冠心病相关的基因中过度富集(P = 0.002)。以β1 - 肾上腺素能受体基因(ADRB1)为中心,该复合物包括18个尚未被确定为冠心病候选位点的蛋白质相互作用伙伴。在顶级复合物中的19个基因中,基于基因敲除小鼠,有5个基因参与异常心血管系统生理特征(富集4倍;Fisher精确检验,P = 0.006)。Ingenuity通路分析显示,顶级复合物中的基因显著富集于典型通路,尤其是与血压调节相关的通路。

结论

将GWA研究与PPI数据相结合成功鉴定出一组冠心病发病的候选易感基因,这些基因在单标记GWA分析中会被遗漏。

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